I'm working on a project now that's rather unlike anything I've done before. I have two tests with binary results that will be administered to the same sample, which is drawn from a clustered population (i.e., some subjects will be from the same family). I'd like to compare proportions of positive test results, but the clustering makes McNemar's test inappropriate so I've been reading up on alternative approaches. The two main routes seem to be 1) the clustering-adjusted McNemar alternatives by Rao and Scott (1992), Eliasziw and Donner (1991), and Obuchowski (1998), and 2) GEE.

Do you know of any implementations of the Rao-Obuchowski lineage in R (or, I suppose, SAS)? GEE is easy to find, but have you had a positive or negative experience with any particular packages? Is there another route to analyzing these data that I'm completely missing?

Thanks in advance for your help - let me know if any clarification is needed.

You could always just use a clustered bootstrap. Resample across families, which you believe are independent. That is, keep families together when you resample. Compute p2 - p1 for each sample. After 1000 iterations or so, compute the upper and bottom 2.5% quantiles. This will give you a bootstrapped 95% confidence interval. Alternatively compute the fraction of samples above zero, or whatever your hypothesis is. The procedure should have good pretty good properties unless the number of families is small.

It's probably easiest to do this by hand in R rather than relying on any package.